1. Field
This invention relates generally to a satellite communications system employing iterative equalization to improve system performance and, more particularly, to a satellite communications system that employs iterative equalization to improve system performance in a system receiver by employing a small number of Volterra series coefficients in a non-linear soft interference cancellation (SIC) and minimum mean square error (MMSE) equalizer.
2. Discussion of the Art
Satellite communications is seeing a growing demand for greater throughput and transponders with more DC power efficiency. To provide greater spectral efficiency, satellite communications systems often employ modern coding and modulation, best exemplified by the DVB-S2 standard. To meet the radiated power demands necessary for the signal transmission distances, satellite communications systems employ high power amplifiers (HPAs), such as traveling-wave tube amplifiers (TVVTAs) and solid-state power amplifiers (SSPA). To provide high throughput and increased efficiency, these HPAs often operate at or near their saturation level, which often results in severe non-linear distortions of the transmitted signal that has a reverse effect on the throughput and performance of the communications channel. Thus, a satellite communications channel provides significantly different design challenges from traditional terrestrial channels due to its dominant non-linear behavior.
It has been shown in the satellite communications art that by applying the concept of “Turbo Processing,” a practical decoding strategy in the system receiver can perform iterative processing between a soft-input/soft-output (SISO) equalizer and an outer channel decoder. For example, a maximum-likelihood sequence detection (MLSD) receiver has been proposed that models a non-linear satellite channel as a finite-state machine (FSM), where the complexity of the model grows exponentially with data throughput. Approximate MLSD receiver structures have been investigated in the art, but the receiver complexity is still O(2McL), where Mc denotes the number of bits per coded symbol and L is the length of the channel memory.
Volterra series decomposition is a known mathematical model for modeling non-linear behavior and provides an efficient and analytically tractable way to represent a non-linear satellite communications channel. Generally, a Volterra series is an infinite polynomial description of a signal. Practical non-linear equalization utilizing a Volterra series representation has been investigated in the satellite communications art, where the non-linear equalizer is constructed in a noiseless environment and in an uncoded situation. Despite showing improvement by non-linearly combining channel observations according to dominant Volterra series coefficients, the non-linear equalizers employing Volterra coefficients proposed in the art suffer significant performance degradation due to noise enhancement in the low signal-to-noise ratio (SNR) regime.
Other practical equalizer/detector structures have been proposed in the satellite communications art to correct non-linear distortions in satellite communications signals including a symbol-by-symbol non-linear de-mapper that models the non-linear satellite communications channel into an effective non-linear inter-symbol interference (ISI) channel. Because this approach is limited by the model accuracy, it suffers extra performance degradation with a strong non-linearity.
Another equalizer has been proposed in the satellite communications art to correct non-linear distortions in satellite communications signals that includes a set of parallel interference cancellation filters for each term in the Volterra series. However, this approach is computationally inefficient both due to the interference cancelling structure and because it uses all the terms in the Volterra series model.
A non-linear complexity SISO detector/equalizer has also been proposed in the satellite communications art to correct non-linear distortions in satellite communications signals based on a heuristic reduced 3rd order Volterra series model, where the reduction of the Volterra series model is independent of the actual non-linear device. This characteristic is undesirable as the terms that most impact performance in the Volterra model are device dependent.